Image alignment (or image registration or image fusion) is used in follow-up image analysis to provide structure-wise or voxel-wise comparison of medical images acquired at different disease states. Visual inspection by a physician can be done, for example, by comparing a baseline image dataset and a follow-up image dataset. One approach includes displaying the two image datasets side-by-side, with or without a marker such as a cross hair overlaid at corresponding anatomical positions in the two image datasets. With this approach, the reader navigates to every potentially suspicious anatomical position followed by visual side-by-side comparison of the underlying structure.
Another approach includes alternately displaying the two aligned image datasets in a same image viewing window. Any differences identified during the comparison are likely due to pathological changes (e.g., a lesion has shown up or a tumor has shrunk) and are therefore of high importance for therapy management. With this approach, the comparison is completely delegated to the human eye, and the image reader navigates to every potentially suspicious anatomical position. The decision whether a pathological change is present is greatly supported by the human eye's capability of quickly detecting differences/movements and changes in presence, size or volume.
Yet another approach includes displaying the two aligned image datasets fused using a different color for each image dataset. Voxel intensities that are the same in both image datasets are displayed using gray-scale coding. A typical output shows most of the image datasets in gray-scale and only structures with changes in color. This approach requires image datasets that are acquired with the same imaging modality and protocol. Otherwise, parts of the fused image datasets will be displayed falsely using a color-coding scheme.
Unfortunately, all of the above comparison approaches tend to require a large amount of reading time by the physician, which could be otherwise spent with patients. In view of at least the above, there is an unresolved need for other approaches for comparing image datasets.